Jump to ContentJump to Main Navigation
Show Summary Details
More options …

The International Journal of Biostatistics

Ed. by Chambaz, Antoine / Hubbard, Alan E. / van der Laan, Mark J.


IMPACT FACTOR 2018: 1.309

CiteScore 2018: 1.11

SCImago Journal Rank (SJR) 2018: 1.325
Source Normalized Impact per Paper (SNIP) 2018: 0.715

Mathematical Citation Quotient (MCQ) 2018: 0.03

Online
ISSN
1557-4679
See all formats and pricing
More options …

Instrumental Variables vs. Grouping Approach for Reducing Bias Due to Measurement Error

Evridiki Batistatou / Roseanne McNamee
Published Online: 2008-06-03 | DOI: https://doi.org/10.2202/1557-4679.1087

Attenuation of the exposure-response relationship due to exposure measurement error is often encountered in epidemiology. Given that error cannot be totally eliminated, bias correction methods of analysis are needed. Many methods require more than one exposure measurement per person to be made, but the `group mean OLS method,' in which subjects are grouped into several a priori defined groups followed by ordinary least squares (OLS) regression on the group means, can be applied with one measurement. An alternative approach is to use an instrumental variable (IV) method in which both the single error-prone measure and an IV are used in IV analysis. In this paper we show that the `group mean OLS' estimator is equal to an IV estimator with the group mean used as IV, but that the variance estimators for the two methods are different. We derive a simple expression for the bias in the common estimator which is a simple function of group size, reliability and contrast of exposure between groups, and show that the bias can be very small when group size is large. We compare this method with a new proposal (group mean ranking method), also applicable with a single exposure measurement, in which the IV is the rank of the group means. When there are two independent exposure measurements per subject, we propose a new IV method (EVROS IV) and compare it with Carroll and Stefanski's (CS IV) proposal in which the second measure is used as an IV; the new IV estimator combines aspects of the `group mean' and `CS' strategies. All methods are evaluated in terms of bias, precision and root mean square error via simulations and a dataset from occupational epidemiology. The `group mean ranking method' does not offer much improvement over the `group mean method.' Compared with the `CS' method, the `EVROS' method is less affected by low reliability of exposure. We conclude that the group IV methods we propose may provide a useful way to handle mismeasured exposures in epidemiology with or without replicate measurements. Our finding may also have implications for the use of aggregate variables in epidemiology to control for unmeasured confounding.

Keywords: exposure measurement error; grouping approach; aggregate variables; instrumental variables

About the article

Published Online: 2008-06-03


Citation Information: The International Journal of Biostatistics, Volume 4, Issue 1, ISSN (Online) 1557-4679, DOI: https://doi.org/10.2202/1557-4679.1087.

Export Citation

©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston.Get Permission

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

[1]
Dong-Hee Koh, Sun-Woo Lee, Byeong-Jin Ye, and Jung-Il Kim
Journal of Occupational and Environmental Hygiene, 2018, Volume 15, Number 5, Page 413
[3]
Marc G. Weisskopf and Thomas F. Webster
Epidemiology, 2017, Volume 28, Number 5, Page 635
[4]
Sharla A. Smith, Glen P. Mays, Holly C. Felix, J. Mick Tilford, Geoffrey M. Curran, and Michael A. Preston
American Journal of Public Health, 2015, Volume 105, Number 9, Page e48
[5]
Tobias Schlueter and Soenke Sievers
Review of Quantitative Finance and Accounting, 2014, Volume 42, Number 3, Page 535
[6]
Evridiki Batistatou and Roseanne McNamee
Statistics in Medicine, 2012, Volume 31, Number 28, Page 3467

Comments (0)

Please log in or register to comment.
Log in